Verification and Correction on ASCAT Wind Velocities Within the Offshore East China Sea

Based on ASCAT wind velocities, observations of 14 meteorological buoys in the offshore East China Sea, and 249 automatic weather stations (AWS) along coastal Zhejiang Province from 2010 to 2014, verification and correction methods are implemented on ASCAT wind velocities and buoy observations. The analysis indicates ASCAT wind velocities are overestimated for all the 14 buoys in comparison with observations, but only 5 of them, all located off Zhoushan Archipelago, hold deviations greater than 2 m·s^-1 with mean bias of 4.79 m·s^-1, and the mean bias for the rest buoys is only 0.46 m·s^-1. Results also imply ASCAT wind velocities are not only related to distances away from the coastal line, but also to the local terrains. Regression methods are applied to investigate relations between ASCAT wind velocities and observations at all the buoys with regression and independent test samples ratio of 70% to 30%. It shows that linear regression can help reduce ASCAT wind deviations at all the buoys, decreasing the mean bias from 2.02 m·s^-1 down to 0.14 m·s^-1, especially at those stations with big errors. The relation of ASCAT deviations among buoys is also studied, indicating there is a positive correlation between the ASCAT wind errors and distances for buoys within 160 km, the closer the distances between buoys are, the bigger the coefficients are, with the logarithmic fitting taking advantages of the linear fitting. Two methods, namely regression and deviation, are carried out to make corrections on ASCAT wind velocities, with effective radius taken into account while doing inverse distance weighing interpolations. Results show the mean deviations and root mean square errors decrease obviously after revision, two methods reduce the mean biases by 1.86 m·s^-1 (67.9%) and 1.74 m·s^-1 (64.2%), and reduce the root mean square errors by 1.19 m·s^-1 (29.2%) and 0.89 m·s^-1 (29.6%), repectively. Case study on the regression method is carried out with corrected ASCAT wind velocities compared with the 10 m wind fields at lead time 0 h of European Centre for Medium-Range Weather Forecasts (ECMWF) fine model (resolution of 0.25°×0.25°). It shows that two methods are proved positive and can help decrease mean wind deviation. Further analysis shows that the deviation method gets the least mean deviation when AWS observations are taken into account, implying that the enhancement of station resolution can help increase the correction result.